• Ujjwal Rai
• Amarjeet Kumar
• Khushwant Kaur Rai

This research paper presents a machine learning- based predictive model for the detection of heart blockage. Heart blockage is a significant cardiovascular condition that requires timely and accurate diagnosis for effective management. Leveraging machine learning techniques, specifically Logistic Regression, this study aims to contribute to the advancement of heart blockage detection. The trained logistic regression model exhibits promising performance, achieving an 85% accuracy on the training dataset and approximately 82% accuracy on the test dataset. By utilizing a diverse dataset of cardiovascular imaging data, this research demonstrates the potential of machine learning in augmenting traditional diagnostic methods for heart blockage detection. The findings underscore the importance of integrating machine learning into cardiovascular diagnostics, thereby enhancing patient care and healthcare system efficiency. As medical data availability continues to grow and machine learning techniques evolve, this research contributes to ongoing efforts to improve heart blockage diagnosis and management.

"Heart Blockage Detection Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.p732-p736, April-2024, Available :http://www.jetir.org/papers/JETIR2404G95.pdf

"Heart Blockage Detection Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppp732-p736, April-2024, Available at : http://www.jetir.org/papers/JETIR2404G95.pdf